Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Artificial Intelligence driven smart operation of large industrial complexes supporting the net-zero goal: Coal power plants

The true potential of artificial intelligence (AI) is to contribute towards the performance enhancement and informed decision making for the operation of the large industrial complexes like coal power plants. In this paper, AI based modelling and optimization framework is developed and deployed for the smart and efficient operation of a 660 MW supercritical coal power plant. The industrial data under various power generation capacity of the plant is collected, visualized, processed and subsequently, utilized to train artificial neural network (ANN) model for predicting the power generation. The ANN model presents good predictability and generalization performance in external validation test with R2 = 0.99 and RMSE =2.69 MW. The partial derivative of the ANN model is taken with respect to the input variable to evaluate the variable’ sensitivity on the power generation. It is found that main steam flow rate is the most significant variable having percentage significance value of 75.3 %. Nonlinear programming (NLP) technique is applied to maximize the power generation. The NLP-simulated optimized values of the input variables are verified on the power generation operation. The plant-level performance indicators are improved under optimum operating mode of power generation: savings in fuel consumption (3 t/h), improvement in thermal efficiency (1.3 %) and reduction in emissions discharge (50.5 kt/y). It is also investigated that maximum power production capacity of the plant is reduced from 660 MW to 635 MW when the emissions discharge limit is changed from 510 t/h to 470 t/h. It is concluded that the improved plant-level performance indicators and informed decision making present the potential of AI based modelling and optimization analysis to reliably contribute to net-zero goal from the coal power plant

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector

Data Driven Model Improved by Multi-Objective Optimisation for Prediction of Building Energy Loads

Machine learning (ML) has been recognised as a powerful method for modelling building energy consumption. The capability of ML to provide a fast and accurate prediction of energy loads makes it an ideal tool for decision-making tasks related to sustainable design and retrofit planning. However, the accuracy of these ML models is dependent on the selection of the right hyper-parameters for a specific building dataset. This paper proposes a method for optimising ML models for forecasting both heating and cooling loads. The technique employs multi-objective optimisation with evolutionary algorithms to search the space of possible parameters. The proposed approach not only tunes single model to precisely predict building energy loads but also accelerates the process of model optimisation. The study utilises simulated building energy data generated in EnergyPlus to validate the proposed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

Advanced analytics for process analysis of turbine plant and components

This research investigates the use of an alternate means of modelling the performance of a train of feed water heaters in a steam cycle power plant, using machine learning. The goal of this study was to use a simple artificial neural network (ANN) to predict the behaviour of the plant system, specifically the inlet bled steam (BS) mass flow rate and the outlet water temperature of each feedwater heater. The output of the model was validated through the use of a thermofluid engineering model built for the same plant. Another goal was to assess the ability of both the thermofluid model and ANN model to predict plant behaviour under out of normal operating circumstances. The thermofluid engineering model was built on FLOWNEX® SE using existing custom components for the various heat exchangers. The model was then tuned to current plant conditions by catering for plant degradation and maintenance effects. The artificial neural network was of a multi-layer perceptron (MLP) type, using the rectified linear unit (ReLU) activation function, mean squared error (MSE) loss function and adaptive moments (Adam) optimiser. It was constructed using Python programming language. The ANN model was trained using the same data as the FLOWNEX® SE model. Multiple architectures were tested resulting in the optimum model having two layers, 200 nodes or neurons in each layer with a batch size of 500, running over 100 epochs. This configuration attained a training accuracy of 0.9975 and validation accuracy of 0.9975. When used on a test set and to predict plant performance, it achieved a MSE of 0.23 and 0.45 respectively. Under normal operating conditions (six cases tested) the ANN model performed better than the FLOWNEX® SE model when compared to actual plant behaviour. Under out of normal conditions (four cases tested), the FLOWNEX SE® model performed better than the ANN. It is evident that the ANN model was unable to capture the “physics” of a heat exchanger or the feed heating process as a result of its poor performance in the out of normal scenarios. Further tuning by way of alternate activation functions and regularisation techniques had little effect on the ANN model performance. The ANN model was able to accurately predict an out of normal case only when it was trained to do so. This was achieved by augmenting the original training data with the inputs and results from the FLOWNEX SE® model for the same case. The conclusion drawn from this study is that this type of simple ANN model is able to predict plant performance so long as it is trained for it. The validity of the prediction is highly dependent on the integrity of the training data. Operating outside the range which the model was trained for will result in inaccurate predictions. It is recommended that out of normal scenarios commonly experienced by the plant be synthesised by engineering modelling tools like FLOWNEX® SE to augment the historic plant data. This provides a wider spectrum of training data enabling more generalised and accurate predictions from the ANN model

Holistic modelling techniques for the operational optimisation of multi-vector energy systems

Modern district energy systems are highly complex with several controllable and uncontrollable variables. To effectively manage a multi-vector district requires a holistic perspective in terms of both modelling and optimisation. Current district optimisation strategies found in the literature often consider very simple models for energy generation and conversion technologies. To improve upon the state of the art, more realistic and accurate models must be produced whilst remaining computationally and mathematically simple enough to complete within short periods. Therefore, this paper provides a comprehensive review of modelling techniques for common district energy conversion technologies including Power-to-Gas. In addition, dynamic building modelling techniques are reviewed as buildings must be considered active and flexible participants in a district energy system. In both cases, a specific focus is placed on artificial intelligence-based models suitable for implementation in the real-time operational optimisation of multi-vector systems. Future research directions identified from this review include the need to integrate simplified models of energy conversion units, energy distribution networks, dynamic building models and energy storage into a holistic district optimisation. Finally, a future district energy management solution is proposed. It leverages semantic modelling to allow interoperability of heterogeneous data sources to provide added value inferencing from contextually enriched informatio

Application of probabilistic deep learning models to simulate thermal power plant processes

Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions